Outreach

We continue to generate public-facing work related to the DSC-WAV project (last updated October 12, 2023).

New courses

• MTH 190 - Introduction to Data Science
  Fall 2022, Holyoke Community College
  Nicholas Schwab and Eric Carreira

Educational materials

• Screencasts about Scrum
  Benjamin S. Baumer, Smith College
  • Scrum roles
  • Sprint mechanics
  • Kanban boards
  • User Stories
  • Code Review

Papers

• Fostering Better Coding Practices for Data Scientists
  Harvard Data Science Review, (Pruim, Gîrjău, and Horton 2023)

• Data Science Transfer Pathways From Associate’s to Bachelor’s Programs
  Harvard Data Science Review, (Baumer and Horton 2023)

• Facilitating Team-Based Data Science: Lessons Learned from the DSC-WAV Project
  Special issue on Data Science Education Research
  Foundations of Data Science, (Legacy et al. 2022)

  • Supplementary Materials:
    • Cohort 1 onboarding agenda
    • Cohort 2 onboarding agenda (Part I, Part II)
    • Cohort 4 onboarding agenda
    • Cohort 1 student interview questions
    • Cohort 1 faculty interview questions
    • Cohort 2 student survey questions
    • Cohort 3 student survey questions
    • Cohort 3 student interview questions
    • Cohort 3 faculty interview questions

• The Data Science Corps Wrangle-Analyze-Visualize program: Building Data Acumen for Undergraduate Students
  Harvard Data Science Review, (Horton et al. 2021)

Workshops

• Facilitating Team-Based Data Science: Agile and Scrum for Undergraduates
  Nicholas J. Horton, Randi L. Garcia, and Chelsey Legacy
  Workshop at US Conference on Teaching Statistics
  May 31, 2023

• Best practices for teaching an introductory data science course
  Nicholas J. Horton and Benjamin S. Baumer
  Workshop at Electronic Conference on Teaching Statistics
  May 19, 2022

• Facilitating Team-Based Data Science: Lessons Learned from the DSC-WAV Project
  Benjamin S. Baumer, Nicholas J. Horton, Andrew Zieffler, and Chelsey Legacy
  Breakout session at US Conference on Teaching Statistics
  July 1, 2021

Talks and Posters

By students

• Analysis of Abortion Rights Fund of Western Massachusetts Data
  Eunice Kim ’22, Christina Sherpa ’23, Claire Bunn ’21, Dianne Caravela ’22, Natalia Iannucci ’22, Smith College
  Celebrating Collaborations
  May 5, 2020
  (virtual poster)

• Mapping Census Data in Springfield
  Grace Hartley ’23J, Sunni Raleigh ’23J, Seren Smith ’22, Sarah Bingham AC ’22, Michelle Flesaker ’22, Smith College
  Celebrating Collaborations
  May 6, 2020
  (virtual presentation)

• Analysis of Abortion Rights Fund of Western Massachusetts Data
  Natalia Ianucci and Claire Bunn and Diane Caravela and Eunice Kim, Smith College
  Voices of Data Science
  University of Massachusetts
  February 20, 2021
  (virtual poster)

• Data Science Corps - Wrangle, Analyze, Visualize: Project VentureWell
  Emma Semenuk Scott, Smith College
  Women in Data Science
  Oct 1, 2020
  (virtual poster)

By faculty

• Teaching Reproducibility and Responsible Workflow
  Nicholas J. Horton (Amherst College)
  Joint Statistical Meetings, Toronto, Canada
  August 8, 2023

• AALAC Workshop: Data Science in the Liberal Arts
  Nicholas J. Horton (Amherst College) and Valerie Barr (Bard College)
  Bryn Mawr College
  June 29, 2023

• Project Based Learning Workshop
  Emily Griffith (NCSU), Nicholas J. Horton (Amherst College), and Mark Ward (Purdue)
  American Statistical Association Project Based Learning series (virtual)
  May 11, 2023

• Ramps & Pathways to Data Science: K12, Community Colleges, and Minority-serving Institutions
  Benjamin S. Baumer, Smith College
  Academic Data Science Alliance Data Science Leadership Summit (Boston University)
  May 9, 2023

• Teaching Reproducibility and Responsible Workflow
  Nicholas J. Horton, Amherst College
  ProDaBi: Paderborn Colloquium on Data Science and Artificial Intelligence in School
  January 18, 2023

• Teaching Reproducibility and Responsible Workflow
  Nicholas J. Horton, Amherst College
  CANSSI Ontario Data Science ARES
  January 16, 2023

• Best Practices for Teaching Introductory Data Science
  Nicholas J. Horton, Amherst College and Benjamin S. Baumer, Smith College
  American Mathematical Association of Two-Year Colleges Annual Conference (Virtual Days) December 2, 2022

• Better Data Tools Foster Better Data Science Education (slides and materials) Nicholas J. Horton, Amherst College NextGen: Data Science Day November 12, 2022

• Data Science Corps: Wrangle, Analyze, Visualize – Experiential Learning in Local Community Organizations
  Valerie Barr, Bard College
  HDR: From Harnessing the Data Revolution to Harvesting the Data Revolution
  October 26, 2022

• Data Science Transfer Pathways from Associate’s to Bachelor’s Programs
  Benjamin S. Baumer, Smith College
  Data and AI Meeting of RI Regional Academic Institutions
  September 16, 2022

• Opening talk: Data science education and workforce needs
  Nicholas J. Horton, Amherst College
  National Academies Broadening Data Science Education for the Future Biomanufacturing Workforce: A Meeting of Experts November 5, 2021

• Data Science Corps - Wrangle, Analyze, Visualize: Experiential Learning in Community Organizations
  Benjamin S. Baumer, Maddie DelVicario
  Liberal Arts Luncheon, Smith College
  October 7, 2021

• The Data Science Corps Wrangle/Analyze/Visualize (DSC-WAV) project: Building data science curricula and connections between two- and four- year institutions
  Nicholas J. Horton, Amherst College
  American Mathematics Association of Two Year Colleges (AMATYC) seminar series September 2, 2021

• Students’ perspectives on entering a data science career after experiential learning with local community organizations
  Vimal Rao, Chelsey Legacy, and Andrew Zieffler
  poster at US Conference on Teaching Statistics
  July 1, 2021

• The importance of good coding practices for data scientists
  Randall Pruim, Maria-Cristiana Gîrjău, and Nicholas J. Horton
  Symposium on Data Science and Statistics
  June 3, 2021

• Keynote talk: Transparent and reproducible analysis as a key component of data acumen
  Nicholas J. Horton, Amherst College
  Project TIER 2021 Spring Symposium: Instruction in Reproducible Research
  April 23, 2021

• The Data Science Corps Wrangle/Analyze/Visualize (DSC-WAV) project: Building data science curricula and connections between two- and four- year institutions
  Nicholas J. Horton, Amherst College
  New England Mathematics Association of Two Year Colleges (NEMATYC) Annual Meeting
  April 9, 2021

• The DSC-WAV project: NSF-funded workforce development in data science
  Nicholas J. Horton, Amherst College
  Holyoke Community College
  Holyoke, MA
  Apr 13, 2020

• The DSC-WAV project: NSF-funded workforce development in data science
  Benjamin S. Baumer, Smith College
  ResearchBytes
  Amherst, MA
  Dec 2, 2019

Software

• dscstyle R package
• valleybikeData R package

Media

• September, 2022 Amstat News: Symposium Focuses on Opportunities for Massachusetts Community Colleges
• WAV: Smith’s Data Science Corps
• Data Science DSC-WAV STEM Dive entry:

• Eben Afarikumah Raises the Bar for GCC’s Computer Information Systems Program

References

Baumer, Benjamin S., and Nicholas J. Horton. 2023. “Data Science Transfer Pathways from Associate’s to Bachelor’s Programs.” Harvard Data Science Review 5 (1). https://doi.org/10.1162/99608f92.e2720e81.

Horton, Nicholas J., Benjamin S. Baumer, Andrew Zieffler, and Valerie Barr. 2021. “The Data Science Corps Wrangle-Analyze-Visualize Program: Building Data Acumen for Undergraduate Students.” Harvard Data Science Review 3 (1): 1–8. https://doi.org/10.1162/99608f92.8233428d.

Legacy, Chelsey, Andrew Zieffler, Benjamin S. Baumer, Valerie Barr, and Nicholas J. Horton. 2022. “Facilitating Team-Based Data Science: Lessons Learned from the DSC-WAV Project.” Foundations of Data Science, May. https://doi.org/10.3934/fods.2022003.

Pruim, Randall, Maria-Cristiana Gîrjău, and Nicholas Jon Horton. 2023. “Fostering Better Coding Practices for Data Scientists.” Harvard Data Science Review 5 (3). https://doi.org/10.1162/99608f92.97c9f60f.

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Created by Ben Baumer. This material is based upon work supported by the National Science Foundation under Grant Numbers HDR DSC 1924017, 1923388, 1923700, 1923934, and 1924032. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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